Actual Irrigation Research Articles

Study on mapping method of irrigated cultivated land–taking Nebraska as an example

The distribution information of irrigated cultivated land is important to many studies, including grain yield estimation, water resources management, drought risk assessment, and so on. This study developed a feature variable, called the irrigation probability index (IPI), based on the principle that irrigation can slow down or inhibit the evolution from meteorological drought to agricultural drought. Nebraska, which has a good irrigation information database, was selected as the study area. Combining IPI with the irrigation statistical data and other selected feature variables, irrigated cultivated land in Nebraska was mapped in 2017 using both the spatialization method and remote sensing classification methods (random forest and support vector machine). We verified the effectiveness of IPI in identifying irrigated cultivated land, analyzed the importance of different feature variables for irrigated cultivated land identification, and compared the accuracy of different irrigation mapping methods. The results show that the IPI is significantly correlated with the actual irrigation area, the area of irrigation facilities, and the number of active irrigation wells. It has a better indicative ability of irrigation in areas with a suitable climate or arid areas, than in areas with a humid climate. The crop water stress index (CWSI), IPI, vegetation index (EVI and NDVI), and land surface temperature difference between day and night are the most important feature variables that are used to distinguish irrigated cultivated land from rain-fed cultivated land. There are differences between the recognition accuracies of the three mapping methods in different climatic regions. Overall, the spatialization method had the highest accuracy (overall accuracy = 86.92 %, kappa = 0.74) and the highest similarity with the existing high-resolution irrigation maps. This study provides a direct and useful reference for other researchers to select feature variables and mapping methods in irrigated cultivated land mapping research.

Optimizing the water-ecosystem-food nexus of avocado plantations

Climate change, food and water security and ecosystem sustainable management are tightly interlinked and require holistic approaches to achieve solutions that do not impact adversely one-another. The objective of this work was to conduct studies, collect data and assess the Water-Ecosystem-Food (WEF) nexus in avocado plantations in the Mediterranean region systematically to minimize the environmental footprint while maximizing the benefits for the farmer and the environment. The study includes two distinct experiments; the first addresses the impact of soil organic amendments addition to optimize the WEF nexus and the second monitors experimentally crop water needs and thus illustrates how irrigation practices aided by technology can reduce substantially water consumption. The results showed that organic amendments addition improves fertility, nutrient sequestration and structure but only had a weak effect on biodiversity by increasing the number of unique species. For the development of an efficient irrigation system it is necessary to determine the radius around the tree, the depth of the roots and the time required for the water to reach the active root zone to determine the amount and duration of irrigation. In this way sufficient water will be added to replenish the soil moisture deficit created due to the evapotranspiration. HYDRUS-1D model was used to simulate soil moisture and the hydrologic budget of an avocado tree located in Koiliaris river basin and confirm the percolation losses to groundwater. The results of this study showed that the actual irrigation needs of avocados in the Mediterranean is less than 2,000 m3/ha which is 75% less than what is recommended and could become the primary measure for the mitigation of climate change impacts especially in semi-arid regions such as the Mediterranean.

Cowpea (Vigna unguiculata L. Walp) morpho-physiological and yield responses to chemical, organic, and biofertilizers at various watering levels utilizing drip irrigation system

ABSTRACT The infertility of sandy soil hampers the growth of many crops. Typically, chemical fertilizers are used to handle this issue, but they are expensive and have deleterious implications on the ecosystem. A two-year field investigation was carried out on cowpea cultivated in sandy soil to assess the effects of different watering levels (70, 80, 90, and 100% of the actual irrigation requirements), chemical fertilizers, organic fertilizers, and biofertilizer (Bradyrhizobium sp.), and their combinations on the morpho-physiological traits and yield of cowpea (Vigna unguiculata L. Walp, cv. Giza 18). Overall, the findings showed that cowpea osmoregulatory molecules (sugars, proteins, and proline) demonstrated greater accumulation, with the content depending on the watering level. The applied fertilizers demonstrated improved cowpea performance at the regular watering level, with chopped potato tops outperforming other fertilizer formulations. Furthermore, the combination of water stress treatments and fertilizer formulations was impactful in relieving the detrimental consequences of water stress as it enhanced plant growth performance, production efficiency, metabolic homeostasis, nutritional value, and mineral ions composition. Subsequently, our results demonstrated agro-wastes, particularly chopped potato tops, could be an effective substitute for chemical fertilizers in sandy soils to sustainably increase soil fertility as well as cowpea yield performance.

Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2

Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.

The Temporal-Stability-Based Irrigation MAPping (TSIMAP) Method: A Virtuous Trade-Off between Accuracy, Flexibility, and Facility for End-Users

Remote sensing technology is an essential tool for tracking human-induced alterations on the water cycle, among which irrigation prevails. The possibility of obtaining detailed and accurate information on the actual irrigation extent through remote-sensing-based approaches is of paramount importance for water resources management. In this study, an update of the TSIMAP (Temporal-Stability-derived Irrigation MAPping) method, originally developed with satellite soil moisture as an input, is proposed. To demonstrate that the flexibility of the approach does not affect its main strength point (i.e., good accuracy in the face of high simplicity for users), a dual analysis relying on 1 km NDVI (Normalised Difference Vegetation Index) instead of soil moisture is carried out over the Ebro basin (Spain); data delivered by the Copernicus Global Land Service (CGLS) are used. First, results of this work are compared with outcomes from the method’s original implementation obtained over a focus area (denominated “Ebro_CATAR”) through satellite soil moisture. In the proposed configuration relying on NDVI, an overall accuracy (OA) up to 93% is found. Results highlight an increase in OA ranging from +2% to +6% depending on the validation strategy with respect to the TSIMAP implementation relying on soil moisture. Then, a basin-scale application is performed, providing performances still satisfactory (OA = 75%) notwithstanding a higher degree of heterogeneity.

How to account for irrigation withdrawals in a watershed model

Abstract. In agricultural areas, the downstream flow can be highly influenced by human activities during low-flow periods, especially during dam releases and irrigation withdrawals. Irrigation is indeed the major use of freshwater in the world. This study aims at precisely taking these factors into account in a watershed model. The Soil and Water Assessment Tool (SWAT+) agro-hydrological model was chosen for its capacity to model crop dynamics and management. Two different crop models were compared in terms of their ability to estimate water needs and actual irrigation. The first crop model is based on air temperature as the main determining factor for growth, whereas the second relies on high-resolution data from the Sentinel-2 satellite to monitor plant growth. Both are applied at the plot scale in a watershed of 800 km2 that is characterized by irrigation withdrawals. Results show that including remote sensing data leads to more realistic modeled emergence dates for summer crops. However, both approaches have proven to be able to reproduce the evolution of daily irrigation withdrawals throughout the year. As a result, both approaches allowed us to simulate the downstream flow with a good daily accuracy, especially during low-flow periods.

Quantifying and valuing irrigation in energy and water limited agroecosystems

Agriculture in regions with limited water availability is possible because of irrigation. Irrigated croplands are expanding, and irrigation water demand is increasing. Nevertheless, there is a limited understanding of how much water is consumed for irrigation and how effective irrigation increases crop productivity in various climates. In this study, we aim to understand how irrigation water affects crop productivity in different climates. To achieve this goal, we developed a simple approach to quantify irrigation quantities from SMAP satellite soil moisture observations based on a zero-dimensional bucket-type hydrology model. The central assumption is that irrigation quantities can be estimated from the gap between the modeled and observed soil moisture by iteratively providing irrigation as a model input until the soil moisture simulations agree well with the observations. We then used the estimated amount of irrigation to simulate water, energy, and carbon fluxes at two agricultural sites on the west coast of the US: one that was water-limited (Central Valley, CA) and one that was energy-limited (Eugene, OR). An agroecosystem model, AgroIBIS-VSF, was used to conduct simulations. To verify our simulations, we used data from two AmeriFlux Eddy covariance towers at each site. We found that incorporating estimated irrigation amounts into our simulations improved the accuracy of energy balance components and soil moisture predictions, reducing the root-mean-square error of soil moisture predictions by up to 22%. We also discovered that the irrigation value, in terms of increased productivity of actual irrigation water used, is more than five times more valuable at the energy-limited site than at the water-limited site. Soil hydraulic properties have a strong influence on irrigation water valuation. Our study highlights the potential of satellite soil moisture observations to improve our understanding of water productivity in different climates. By better understanding the efficiency of resources used for crop production, we can ensure the sustainability and resilience of agricultural systems, leading to better management practices.

Evaluation of Water Balance and Water Use Efficiency with the Development of Water-Saving Irrigation in the Yanqi Basin Irrigation District of China

Irrigation water is the main type of water consumption in the Yanqi Basin irrigation district of Xinjiang, which is an oasis-type irrigation district in the arid region of Northwest China. With the continuous expansion of cultivated areas, there is an increasing demand for irrigation water, resulting in an irrigation efficiency paradox and the phenomenon of “the more water-saving, the more water-scarce”. In this study, the water balance method and the improved IWMI (International Water Management Institute) water balance method were used with remote sensing and statistical data from 1980 to 2020 to analyze the changes in the irrigation water supply, consumption, and loss for improvement in irrigation water use efficiency (IWUE) in the Yanqi Basin. The results showed that there was an upward trend in the cultivated land area in the irrigation district of Yanqi Basin, as monitored with remote sensing from 1980 to 2020, and the values from the remote sensing data were higher than those from the yearbooks. According to the remote sensing data, the arable land area in the irrigation district increased from 1672 km2 in 1980 to 2494 km2 in 2020, thus showing a trend of expansion. The traditional water use efficiency in the irrigation district showed an increasing trend. The lowest value for the field water-use coefficient was 0.70 in 1998, and it exceeded to 0.81 from 2009 to 2020. The canal water-use coefficient was as low as 0.50 in 1998 and increased from 0.54 in 2009 to 0.82 in 2020. The irrigation water-use coefficient increased from 0.35 in 1998 to 0.68 in 2020, with a general upward trend. In this study, the water consumption ratio indicator DFg (depleted fraction), determined using the improved water balance method, increased from 0.8390 in 1980 to 0.8562 in 2020, although it showed an overall decreasing trend, and the average was 0.8436. Cultivated land’s actual irrigation water consumption per unit area reached the highest value of 8.41 × 106 m3/hm2/a in 2011 and the minimum value of 4.01 × 106 m3/hm2/a in 2020, and from 1980 to 2020 it showed a decreasing trend, while the total water diversion showed an increasing trend due to the continuous expansion of arable land. From 1980 to 2020, water diversion into the irrigation district changed from 1.214 km3 to 1.000 km3, and it reached a maximum of 1.593 km3 in 2000; water diversion into the irrigation district showed an overall upward trend. The positive impact of the post-2000 water conservation phase with the adaptation of water-saving irrigation technology was clear, as the findings showed an increase in IWUE in the Yanqi Basin irrigation district. These results provide a theoretical basis for breaking the paradox of irrigation efficiency, which can be used in the water resource management of irrigation districts.

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.

Application of geospatial technique and hydrological modelling in evaluating reservoir sedimentation in Chhattisgarh state of India

ABSTRACT Soil erosion and sediment deposition in the reservoirs are both consequences of the change in precipitation pattern that has been observed over the past decade. For the purpose of storing water in reservoirs built before the 1980s, the Indian state of Chhattisgarh has a few irrigation projects. As the state has grown, agricultural land has increased while the proportion of forest area has decreased. Due to this development, runoff is introducing more sediment into the reservoirs, resulting in a reduction in intended storage. The effects of sediment deposition in the Kodar reservoir, located in the Mahasamund district of Chhattisgarh State, are evaluated in present study. Prior to five years, the reservoir reported a 30% deficit in actual irrigation. To quantify the sediment load, Remote Sensing (RS), Geographic Information System (GIS), and the Hydrologic Engineering Centre – Hydrologic Modelling System (HEC-HMS) were effectively employed. From 2012 to 2014, the reservoir received an average sediment load of 1.11 million tonnes. In order to preserve soil in the watershed’s erosion-prone area, a treatment plan is developed. It includes numerous thematic maps that are overlaid in order to provide preventive measures against reservoir sedimentation and is further proposed in the form of best management practices.

Optimization algorithm for determining working water head of subsurface irrigation with ceramic emitters based on soil wetting patterns

Appropriate working water head of subsurface irrigation systems with ceramic emitters (SICE) is crucial for providing a stable and suitable soil water environment to promote crop production and save water resources in intensive agriculture. However, working water head optimization of SICE is hindered by the uncertainties of the soil physic properties, increasing the risk of irrational irrigation. To effectively address this challenge, this study presented an optimization algorithm for optimizing the working water head of SICE based on soil wetting patterns, considering taking into account soil physical variables and management variables, and then the reliability of the algorithm was verified by three case studies. The results of three case studies showed that the relative error between the actual irrigation amount of SICE using the optimization algorithm, and the recommended irrigation amount was lower than 5%. In three case studies, the sand-based ceramic emitter and working water head of 40 cm, 30 cm, and 70 cm were recommended parameters for SICE. The crop yield under SICE with appropriate working water head for three case studies was 109.2 × 103 kg ha−1, 5643.3 kg ha−1, and 3182.6 kg ha−1, which were 8.01%, 30.56%, and 16.90% higher than that of subsurface drip irrigation, respectively. Our findings revealed that SICE with the appropriate working water head might be an efficient subsurface irrigation method for intensification agriculture with the goal of high crop production.

Leaching characteristics and potential risk of heavy metals from drip irrigation pipes and mulch substrate in agricultural ecosystems

Drip irrigation is a valuable method for optimising water and fertiliser usage, motivating its increasing use. However, the ecological effects of drip irrigation fertilisation have not been sufficiently evaluated, limiting its effective and widespread use. Within this context, we aimed to determine the effects and potential ecological risks of using polyethylene irrigation pipes and mulch substrate under various drip irrigation conditions as well as burning of waste pipes and mulch substrate. Laboratory simulations of field conditions were used to determine the distribution, leaching, and migration pattern of heavy metals (Cd, Cr, Cu, Pb, and Zn) from plastic drip irrigation pipes and agricultural mulch substrate into various solutions. Maize samples obtained from drip-irrigated fields were analysed to determine the presence of heavy metal residues and assess the risk of heavy metal contamination. Heavy metal leaching from pipes and mulch substrate was high under acidic conditions, while the migration of heavy metals from plastic products was low in alkaline water-soluble fertiliser solutions. After combustion, heavy metal leaching from pipes and mulch residues increased considerably, with the migration capacity of Cd, Cr, and Cu increasing by >10-fold. Heavy metals in plastic pipes migrated primarily to the residue (bottom ash), whereas those from mulch substrate migrated to the fly ash component. Under experimental conditions, the migration of heavy metals from plastic pipes and mulch substrate had a negligible effect on the heavy metal content in aqueous environments. Although heavy metal leaching increased, the effect on water quality under actual irrigation conditions was relatively minor (in the order of 10−9). Thus, the use of plastic irrigation pipes and mulch substrate did not result in significant heavy metal contamination and potential risk to the agriculture ecosystem. Our study findings provide evidence for the effective application and widespread promotion of drip irrigation and fertiliser technology.

Research on Cotton Field Irrigation Amount Calculation Based on Electromagnetic Induction Technology

The rapid and efficient acquisition of field-scale farmland soil profile moisture-distribution information is very important for achieving precise irrigation and the adjustment and deployment of irrigation strategies in farmland. EM38-MK2 is a portable, non-invasive device that induces electric currents in soil to generate secondary magnetic fields for the rapid measurement of apparent electrical conductivity in the field. In this study, cotton fields were used as experimental objects to obtain soil apparent conductivity data for three periods, which were combined with soil-moisture content data collected simultaneously from soil samples and measured in the laboratory to construct an apparent soil-profile moisture regression model. A simple kriging interpolation method was used to map the distribution of the irrigation volume in the field, considering only the highest irrigation volume in the field as the maximum water-holding capacity in the field. The results showed that EM38 could accurately detect the spatial variation of soil moisture in the field. The R2 of the linear fit between measured and predicted soil-water content ranged from 0.51 to 0.89; the RMSE ranged from 0.66 to 1.87; and the R2 and RPD of each soil-layer water content model of the single-period model were higher than those of the full-period model. By plotting the distribution of field irrigation, it could be seen that by comparing the predicted field irrigation with the actual irrigation, at least 160 m3 ha−1 of irrigation could be saved in all three periods at an irrigation depth of 40 cm, which is about 30% of the actual irrigation; at an irrigation depth of 60 cm, about 30% and 15% of irrigation could be reduced in July and August, respectively. There are three areas in the study area with high fixed-irrigation volumes located in the northwest corner, near 500 m in the northern half of the study area and 750 m east of the southern half of the study area. The results of this study proved that the use of EM38-MK2 to monitor and evaluate the soil-moisture content of the farmland at different periods can, to a certain extent, guide the irrigation amount needed to achieve efficient and precise irrigation in the field.

Removal of Pb2+ and Cd2+ from irrigation water and replenishment of mineral nutrients using a low-cost mineral adsorbent derived from potassium-rich aluminum silicates

Heavy metal pollution and a lack of essential mineral nutrients in irrigation water seriously impact crop safety and quality. A low-cost mineral adsorbent (LCMA) was created using mechanochemical methods from illite, wollastonite, gypsum, dolomite, and calcium carbonate (molar ratio of 2:1:1:3:9) to remove Pb2+ and Cd2+ and supplement mineral nutrients in irrigation water. The results showed that LCMA had a higher removal capacity than most similar mineral materials, with maximum adsorption capacities of 278 and 83.1 mg·g–1 (298 K) for Pb2+ and Cd2+, respectively. At pH ≥ 3, there was very little Pb2+ and Cd2+ desorption from LCMA, whereas desorption was simple at pH ≤ 0.5, indicating the chemical stability of LCMA. Pb2+ and Cd2+ are competitive adsorptions, with Pb2+ taking precedence. In actual irrigation water, the removal rates of Pb2+ and Cd2+ by LCMA were approximately 90 % and 80 %, respectively, within 10 min. Complexation and co-precipitation were used to attach Pb2+ and Cd2+ to LCMA, followed by ion exchange and electrostatic interaction. Pb2+ was primarily adsorbed on LCMA as PbAl2O4, Pb2SiO4, and PbSO4, whereas Cd2+ was primarily adsorbed as Cd3Al2Si3O12 and CdSiO3. In addition, with the application of LCMA, the concentrations of Si4+, K+, Ca2+, and Mg2+ in the polluted irrigation water increased by 12.4 %, 22.8 %, 78.9 %, and 439 %, respectively. LCMA is a strong candidate to replace the current expensive remediation technology for heavy metal pollution and lacking mineral nutrients in irrigation water.

Water Footprint Assessment for Irrigated Paddy Cultivation in Walawe Irrigation Scheme, Sri Lanka

Water footprint (WF) is a comprehensive summation of the volume of freshwater consumed directly and indirectly in all the steps of the production chain of a product. The water footprint concept has been widely used in agricultural water resources management. Water for irrigation is supplied in Sri Lanka to farmers at no cost, and thus the question is arising, whether the current management strategies the authorities and the farmers follow are appropriate to achieve productive water utilization. Therefore, this study aims at evaluating the water footprint of rice production in an irrigation scheme in the dry zone of Sri Lanka, the Walawe irrigation scheme. Due to the unreliability of the rainfall in the study area paddy cultivation depends entirely on irrigation, thus, the WFblue, in other terms the volume of water evaporated from the irrigation water supply is considered as the total WF (WFtot) in this study. Actual crop evapotranspiration (equivalent to ETblue) was estimated based on the Penman-Monteith (P-M) model integrating effective rainfall, and crop coefficient published in Sri Lankan Irrigation Design Guidelines. The study spanned for three irrigation years from 2018–2021. Actual irrigation water issued to the field was estimated based on the data recorded by the government body responsible for irrigation water management of the area—Mahaweli Authority of Sri Lanka. The total volume of percolated water was computed employing the water balance method while assuming runoff is negligible. Results show that the average annual WFblue found to be 2.27 m3/kg, which is higher than global and national WFtot. As the crop yield in the study area (6.5 ton/ha) is also higher than the global (4.49 ton/ha) and national (3.5 ton/ha) yields, a conclusion was drawn that the irrigation water usage (CWUTblue) in the area may be significantly higher. It was then noted the higher CWUTblue was due to relatively higher evapotranspiration in the area. Thus, it is vital to reduce excess water usage by shifting irrigation practices from flooded irrigation to the System of Rice Intensification (SRI).

Simulation-optimization based real-time irrigation scheduling: A human-machine interactive method enhanced by data assimilation

Efficient irrigation scheduling is crucial for both improving crop production and saving irrigation water use in arid/semi-arid agricultural regions threatened by water shortage and soil salinity. However, irrigation scheduling optimization is hindered by the uncertainties of data and optimization model, and adopting the optimal irrigation scheduling is subject to farmers' acceptance. To effectively tackle these challenges, this paper presents a novel human-machine interactive framework for real-time irrigation scheduling (RIS). The developed modeling framework couples a simulation-optimization model, irrigation farmers, and a data assimilation procedure within the human-machine interactive framework for RIS. The proposed approach is capable of: 1) searching optimal irrigation scheduling through the simulation-optimization model; 2) making actual irrigation decisions based on farmers' experiences, knowledge, behaviors, or optimal solutions; and 3) updating soil water content based on the model simulations and real-time observations at each time period. The RIS is applied to a real-world case in a typical arid agricultural region of China. Based on the comparisons with historical irrigation records and a tradition simulation-optimization model, the proposed RIS can not only achieve higher economic benefit with less irrigation water allocation quotas, but also improve irrigation efficiency. This study contributes to the methodology by integrating computer model, real-time observations and farmers' experiences to optimization modeling framework for supporting sustainable irrigation water management.

Leveraging remotely-sensed vegetation indices to evaluate crop coefficients and actual irrigation requirements in the water-stressed Maipo River Basin of Central Chile

Assessment of irrigation needs for agricultural production has commonly relied on reference crop coefficients derived using limited climatic conditions. This work focused on evaluating actual crop coefficients (Kc) by leveraging available satellite data from Landsat 8 Operational Land Imager (OLI) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). This method built on the linear relationship and variability between reference Kc from the Food and Agricultural Organization (FAO) and specific site response in Normalized Difference Vegetation Index (NDVI). A regional model was developed for the water-stressed Maipo River Basin of Central Chile during the growing seasons of maize from 2018 to 2022. Throughout model validation, NDVI-based Kc predictions of high quality were found with R-squared scores ranging from 0.77 to 0.97. Relying on Earth observations, this approach provided site-specific Kc values and estimates of actual evapotranspiration without requiring site instruments and/or particular knowledge of farming schedules. Overall, this study has the potential to assist individuals and organizations around the world in improving irrigation management approaches, and advance precision agriculture in the context of water scarcity.

A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets

Irrigated agriculture has undergone rapid developments in China, which has greatly increased food production but overexploited water resources as well. Spatial information on irrigated cropland is critical to balance irrigation yield gains against the negative impact on water resources. However, remote-sensing-based maps on irrigated areas with short temporal coverage often suffer from undermined accuracy in humid areas and inconsistency with statistics, which limit their applications in food policy and water management. The following study integrates existing irrigation maps, observed data on irrigated cropping system, and statistics by a synergy approach to map irrigated areas in China from 2000 to 2019. We also incorporate past information on actual irrigation to avoid divergence between observations and statistics from its fluctuation. Afterwards, 614 reference samples across mainland China have been used to validate resultant maps, which show that outperformance was above overall accuracy and Kappa coefficients. Moreover, our maps share a similar spatial pattern with Irrimap-Syn maps rather than remote-sensing-based maps (CCI-LC). Irrigated areas have grown rapidly from 55.42 Mha in 2000 to 71.33 Mha in 2019 but with different growth trends in different regions. Simultaneous large-scale expansion and abandonment occur in the Huang-Huai-Hai Plain and Yangtze River Basin, while the Northwest Inland Region and the Northeast Plain are the two largest net area gains. Rainfed croplands are dominant sources of expansion, followed by pastures, respectively, with over 70% and 20% contributions in total gains. This not only is a shift from rainfed to irrigated systems but also indicates an intensification of agriculture, which might contribute to agricultural drought reductions in the north and wide soil suitability. Other efforts on agricultural sustainability also have been detected, such as geographical shifts from vulnerable to relatively suitable areas, grain for green, cropland protection, and cropland protection in the competition of urbanization.

Simultaneous removal of organic inorganic composite contaminants by in situ double modified biochar: Performance and mechanisms

BackgroundIn this study, double modified biochar was prepared to remove organic matter and heavy metals at the same time. It has high removal efficiency for organic matter even in actual irrigation water. MethodsTobacco stem was used as carbon-based carrier, modified with polyacrylic acid (PAA) rich in oxygen functional groups (OFGs) and transition metals (iron and cobalt) with excellent catalytic activity. After mixing them evenly, the in-situ double modified biochar (BCPAA@CFO) of OFGs and CoFe2O4 (CFO) was obtained by one-step hydrothermal carbonization (HTC). Significant findingsBCPAA@CFO in QNC-Pb2+composite system, CFO can quickly activate peroxymonosulfate (PMS), and the degradation efficiency of quinclorac (QNC) is as high as 80.7%. Free radical quenching experiments and electron paramagnetic resonance (EPR) analysis indicated that SO4·−, ·OH, ·O2− and 1O2 were the main active species involved in the degradation of QNC. At the same time, the rich OFGs make the adsorption efficiency of Pb2+reach 73.2% within 4 h. FT-IR results further confirmed that the dominant role of adsorption should be OFGs. In general, this in-situ double modified BCPAA@CFO obtained by one pot HTC is a promising purifying agent for mixed pollutants.

Modelling water fluxes to improve banana irrigation scheduling and management in Magdalena, Colombia

In this paper, an irrigation scheduling model for banana (Musa sp.) was developed to simulate crop growth and water fluxes under typical commercial plantation conditions. Whilst generic models exist for scheduling irrigation for many crops, their suitability for bananas are limited because of the asynchronous nature of crop growth. Individual fields on banana plantations typically contain trees at varying stages in their development cycle, so it is important for scheduling to account for this heterogeneity in simulating crop production. A crop modelling approach was developed using field data from Magdalena, an economically important region of banana production in Colombia. Following model development and calibration, irrigation water demand was estimated and weekly irrigation scheduling advice then transmitted by SMS to individual farmers in the region. The model also takes into account farmer feedback on actual irrigation practices to compare against estimated irrigation demands and to train model performance. Despite good model calibration, analysis of irrigation practices from farmer feedback showed only moderate to poor correlation between actual irrigation applications and the scheduling guidance. This implies a reluctance of farmers to change long-established traditional irrigation management practices, despite awareness of the impacts of systematic over-irrigation on yields and increased nutrient leaching risks. Significant ongoing research efforts will be needed to support improved knowledge and practical water management for key plantation crops.